In this study, a speed sensorless algorithm was developed to control a single-link manipulator connected to DC motor. The armature voltage value can be obtained by using duty cycle information generated by the controller without using any sensor. Thus, the proposed system does not require any additional measurement sensor. This paper presents a study for artificial neural network (ANN)-based speed estimation algorithm which has a closed-loop speed control with the first and second inputs generated via support of system identification (SI). The second SI input was obtained as a simple transfer function with discrete time. The performances of the SI-input ANN structure and the conventional extended Kalman filter (EKF) method were compared in the MATLAB/Simulink environment. It was observed that the proposed method revealed better results than the EKF method in the steady and transient states. Thus, it was shown that high-performance sensorless speed control could be performed with SI-ANN structure in applications.

在这项研究中，开发了一种无速度传感器算法来控制连接到直流电动机的单连杆机械手。可以通过使用控制器生成的占空比信息来获得电枢电压值，而无需使用任何传感器。因此，所提出的系统不需要任何附加的测量传感器。本文提出了一种基于人工神经网络（ANN）的速度估计算法的研究，该算法具有闭环速度控制，并通过系统识别（SI）的支持生成了第一和第二输入。获得第二个SI输入作为具有离散时间的简单传递函数。在MATLAB / Simulink环境中比较了SI输入ANN结构和常规扩展卡尔曼滤波器（EKF）方法的性能。观察到，在稳态和瞬态下，所提出的方法显示出比EKF方法更好的结果。因此，表明可以在应用中使用SI-ANN结构执行高性能的无传感器速度控制。